DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This is a non-final Office Action on the merits. Claims 1-9 are currently pending and are addressed below.
Examiner Notes that the fundamentals of the rejections are based on the broadest reasonable interpretation of the claim language. Applicant is kindly invited to consider the reference as a whole. References are to be interpreted as by one of ordinary skill in the art rather than as by a novice. See MPEP 2141. Therefore, the relevant inquiry when interpreting a reference is not what the reference expressly discloses on its face but what the reference would teach or suggest to one of ordinary skill in the art.
Priority
Acknowledgment is made of applicant’s claim priority for JP foreign application JP2023-021099, filed on 02/14/2023 and 371 of PCT/JP2023/046316 filed on 12/25/2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 07/30/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“a sensing information acquiring section” in claims 1 and 9.
“a class information generator” in claims 1, 3 and 9.
“a stationary object detector” in claims 1, 4 and 9.
“a position information calculator” in claims 1 and 9.
“a class filtering section” in claim 4.
“a behavior planning section” in claim 9.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Upon reviewing the specification, the corresponding structure for the “sensing information acquiring section”, “class information generator”, “stationary object detector” , “position information calculator”, “class filtering section” and “behavior planning section” is found, [0022] The information processing apparatus 10 includes hardware, such as a processor including a CPU, a GPU, and a DSP; a memory including a ROM and a RAM; and a storage device including an HDD, that is necessary for a configuration of a computer. For example, an information processing method according to the present technology is performed by the CPU loading, into the RAM, a program according to the present technology that is recorded in, for example, the ROM in advance and executing the program. [0023] For example, the information processing apparatus 10 can be implemented by any computer such as a PC. Of course, hardware such as an FPGA or an ASIC may be used. In the present embodiment, a position information calculator 27 is implemented as a functional block by the CPU executing a specified program. Of course, dedicated hardware such as an integrated circuit (IC) may be used in order to implement the functional block. [0024] The program is installed on the information processing apparatus 10 through, for example, various recording media. Alternatively, the installation of the program may be performed via, for example, the Internet. In the present embodiment, a program used to execute a parking assistance system 100 described later is stored in the ROM and is deployed by the RAM to be executed by a computation section 20. [0025] The type and the like of a recording medium that records therein a program are not limited, and any computer- readable recording medium may be used. For example, any non- transitory computer-readable recording medium may be used. [0026] As illustrated in Fig. 1, the information processing apparatus 10 includes a computation section 20 and a storage 30. The computation section 20 includes an image data acquiring section 21, a group-of-points data acquiring section 22, an environmental data acquiring section 23, a map updater 24, a class information generator 25, a stationary object detector 26, a position information calculator 27, and a class filtering section 28. [0027] The image data acquiring section 21 acquires image data acquired from the camera. For example, the image data includes data of images of objects, such as another vehicle and a person, that move (hereinafter referred to as moving objects), and data of images of objects, such as a dividing line and a parking block in a parking lot, that are less likely to move or to be moved (hereinafter referred to as stationary objects). In the present embodiment, the acquired image data is supplied to the class information generator
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis – Step 1
Claim 1 is directed to a device and claim 8 is directed to a method. Therefore, claims 1 and 8 are within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. The other analogous claim 8 is rejected for the same reasons as the representative claim 1 as discussed here. Claim 1 recites:
An information processing apparatus, comprising:
a sensing information acquiring section that acquires sensing information based on a sensor included in a vehicle;
a class information generator that generates class information for an object included in the sensing information;
a stationary object detector that detects a stationary object from among the objects on a basis of the class information, the stationary object moving with a probability less than or equal to a specified threshold;
and a position information calculator that calculates position information regarding a position of the vehicle on a basis of comparison of position information regarding a position of the stationary object to stored map information.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “generated …”, “detects …” and “calculates …” all the various data in the context of this claim encompasses a person looking at data collected (received, detected, etc.) and forming a simple judgement (determination, analysis, comparison, etc.) either mentally or using a pen and paper. Accordingly, the claim recites at least one abstract idea. The Examiner notes that under MPEP 2106.04(a)(2)(III), the courts consider a mental process (thinking) that "can be performed in the human mind, or by a human using a pen and paper" to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, "methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’" 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)). See also Mayo Collaborative Servs. v. Prometheus Labs. Inc., 566 U.S. 66, 71, 101 USPQ2d 1961, 1965 ("‘[M]ental processes[] and abstract intellectual concepts are not patentable, as they are the basic tools of scientific and technological work’" (quoting Benson, 409 U.S. at 67, 175 USPQ at 675)); Parker v. Flook, 437 U.S. 584, 589, 198 USPQ 193, 197 (1978) (same).
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” while the bolded portions continue to represent the “abstract idea”):
An information processing apparatus, comprising:
a sensing information acquiring section that acquires sensing information based on a sensor included in a vehicle;
a class information generator that generates class information for an object included in the sensing information;
a stationary object detector that detects a stationary object from among the objects on a basis of the class information, the stationary object moving with a probability less than or equal to a specified threshold;
and a position information calculator that calculates position information regarding a position of the vehicle on a basis of comparison of position information regarding a position of the stationary object to stored map information.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations above, the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (processor) to perform the process. In particular, the acquires steps from / using sensor system(s) are recited at a high level of generality (i.e. as a general means of receiving information and casting rays to detect information for use in the determining and other steps), and amounts to mere data gathering, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception (MPEP § 2106.05). Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the 2019 PEG, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform the steps amounts to nothing more than applying the exception using a generic computer component. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the additional limitations discussed above are insignificant extra-solution activities.
The additional limitations of receiving information and values/features detecting/detectable are well-understood, routine and conventional activities because the background recites that the sensors are all conventional sensors, and the specification does not provide any indication that the processor is anything other than a conventional computer. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible.
Dependent claims 2-7 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or additional elements that do not integrate the judicial exception into a practical application. The dependent claims are merely defining elements or have additional steps such as “classifies”, “filtering” and “generates”. Therefore, dependent claims 2-7 are not patent eligible under the same rationale as provided for in the rejection of claim 1.
Therefore, claims 1-8 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Chiba JP2022076876A (English Translation) in view of Vora (US 20230227065 A1).
Regarding Claim 1, Chiba teaches An information processing apparatus, comprising (see at least [¶05, 07-09 & 021]):
a sensing information acquiring section that acquires sensing information based on a sensor included in a vehicle (Acquiring sensing information from camera and LIDAR sensors included in the vehicle. see at least [¶013-014 & 016]);
and a position information calculator that calculates position information regarding a position of the vehicle on a basis of comparison of position information regarding a position of the stationary object to stored map information (Calculating the self-position of the vehicle based in the comparison of the position of the stationary object/landmark to the stored map information. see at least [¶035-036 & 039-040]).
Chiba does not explicitly teach a class information generator that generates class information for an object included in the sensing information; a stationary object detector that detects a stationary object from among the objects on a basis of the class information, the stationary object moving with a probability less than or equal to a specified threshold.
Shall be noted that Chiba teaches to assign type information to sensed information/objects to classify them and can determine stationary landmarks/objects from the sensed objects (see at least [“[¶035] The acquired environment point cloud data is provided with type information based on processing such as semantic segmentation by the LiDAR 11 or the extraction unit 120 or the like.” & “[¶039] Further, the specifying unit 130 may specify the landmark point cloud after removing the point cloud of the moving object from the point cloud within the range.”) For more clarification the examiner is using secondary reference of Costea.
Vora does teach a class information generator that generates class information for an object included in the sensing information (Generating class/type information of an object included in the sensing information. see at least [¶050-051]);
a stationary object detector that detects a stationary object from among the objects on a basis of the class information, the stationary object moving with a probability less than or equal to a specified threshold (Detecting stationary objects from a set of sensed objects based on the object classification and the stationary object moving with a probability/likelihood less than a specified threshold. see at least [¶03, 071 & 074-075]);
Vora would be in a similar field as it also deals in the area of identifying a state of a vehicle using objects around the vehicle. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Chiba to use the technique of having a class information generator that generates class information for an object included in the sensing information; a stationary object detector that detects a stationary object from among the objects on a basis of the class information, the stationary object moving with a probability less than or equal to a specified threshold as taught by Vora. Doing so would lead to improved control of a vehicle depending on the state of the vehicle (see at least [¶075-076]).
Regarding Claim 2, Chiba and Vora teach all of the limitations of claim 1 as shown above, furthermore, Chiba teaches wherein the sensor includes a camera and LiDAR, and the sensing information includes image data and group-of- points data (The sensor includes a camera and LIDAR AND the sensing information includes image data and point cloud data. see at least [¶013-014 & 016]).
Regarding Claim 3, Chiba and Vora teach all of the limitations of claim 2 as shown above, furthermore, Chiba teaches wherein the class information generator classifies the objects into at least one class on a basis of the image data and the group- of-points data (Classifying objects into a landmark class on the basis of image and point cloud data. see at least [¶028-036]).
Regarding Claim 4, Chiba and Vora teach all of the limitations of claim 3 as shown above, furthermore, Chiba teaches further comprising a class filtering section that performs filtering such that the stationary object being from among the objects and detected by the stationary object detector remains (Filtering stationary/landmark objects from the sensed objects and thus keeping only the detected stationary/landmark object. see at least [¶039]).
Regarding Claim 5, Chiba and Vora teach all of the limitations of claim 4 as shown above, furthermore, Chiba teaches wherein the filtering includes excluding the group-of-points data of a group of points of the object other than the stationary object (The filtering includes excluding point cloud data of moving objects and only leaving point cloud data of stationary/landmark objects. see at least [¶039]).
Regarding Claim 6, Chiba and Vora teach all of the limitations of claim 1 as shown above, furthermore, Chiba teaches wherein the class information generator generates the class information using semantic segmentation (The class/type information can be obtained using semantic segmentation. (Semantic segmentation is used in computer vision to classify objects) see at least [¶035]).
Regarding Claim 6, Chiba and Vora teach all of the limitations of claim 1 as shown above, furthermore, Chiba teaches wherein the position information regarding the position of the stationary object includes group-of-points data that indicates a three-dimensional position of the stationary object (Position information of the stationary/landmark object includes point cloud data that includes three-dimensional position of the object/landmark. The three-dimensional information of the object/landmark must be included to compare with the map data. see at least [¶026 & 035-040]),
and the map information includes the group-of-points data of a group of points of the object situated at a specified location (The map information includes point cloud data of the object/landmark at a specified location. see at least [¶026 & 040]).
Regarding Claim 8, Chiba teaches An information processing method that is performed by a computer system, the information processing method comprising (see at least [¶05, 07-09 & 021]):
acquiring sensing information based on a sensor included in a vehicle (Acquiring sensing information from camera and LIDAR sensors included in the vehicle. see at least [¶013-014 & 016]);
and calculating position information regarding a position of the vehicle on a basis of comparison of position information regarding a position of the stationary object to stored map information (Calculating the self-position of the vehicle based in the comparison of the position of the stationary object/landmark to the stored map information. see at least [¶035-036 & 039-040]).
Chiba does not explicitly teach generating class information for an object included in the sensing information; detecting a stationary object from among the objects on abasis of the class information, the stationary object moving with a probability less than or equal to a specified threshold.
Shall be noted that Chiba teaches to assign type information to sensed information/objects to classify them and can determine stationary landmarks/objects from the sensed objects (see at least [“[¶035] The acquired environment point cloud data is provided with type information based on processing such as semantic segmentation by the LiDAR 11 or the extraction unit 120 or the like.” & “[¶039] Further, the specifying unit 130 may specify the landmark point cloud after removing the point cloud of the moving object from the point cloud within the range.”) For more clarification the examiner is using secondary reference of Costea.
Vora does teach generating class information for an object included in the sensing information (Generating class/type information of an object included in the sensing information. see at least [¶050-051]);
detecting a stationary object from among the objects on abasis of the class information, the stationary object moving with a probability less than or equal to a specified threshold (Detecting stationary objects from a set of sensed objects based on the object classification and the stationary object moving with a probability/likelihood less than a specified threshold. see at least [¶03, 071 & 074-075]);
Vora would be in a similar field as it also deals in the area of identifying a state of a vehicle using objects around the vehicle. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Chiba to use the technique of a generating class information for an object included in the sensing information; detecting a stationary object from among the objects on abasis of the class information, the stationary object moving with a probability less than or equal to a specified threshold as taught by Vora. Doing so would lead to improved control of a vehicle depending on the state of the vehicle (see at least [¶075-076]).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Imai JP2022098397A (English Translation) in view of Vora (US 20230227065 A1) in view of Chiba JP2022076876A (English Translation).
Regarding Claim 9, Imai teaches A parking assistance system, comprising (see at least [¶01-02 & 011]):
a vehicle (A vehicle. see at least [¶08 & 038]);
an information processing apparatus that includes a sensing information acquiring section that acquires sensing information based on a sensor included in the vehicle (An information processing device that includes acquiring sensing information from a sensor included in the vehicle. see at least [¶08-010]),
and an automated driving controller that includes a behavior planning section that creates a traveling route on a basis of position information regarding a position of the vehicle, the traveling route connecting the vehicle and a space in which the vehicle is allowed to be parked (An automated/automatic driving controller that creates a traveling route on the basis of the position information of the vehicle, with the traveling route connecting the vehicle to a parking space where the vehicle can park. see at least [¶065-068, 079-083, 0113 & 0136-0137]),
and a movement controller that controls the vehicle on a basis of the traveling route parked (The control unit controls the vehicle based on the traveling route (action plan) to be parked. see at least [¶065-068, 079-083, 0113 & 0136-0137]).
Imai does not explicitly teach a class information generator that generates class information for an object included in the sensing information; a stationary object detector that detects a stationary object from among the objects on a basis of the class information, the stationary object moving with a probability less than or equal to a specified threshold.
Vora does teach a class information generator that generates class information for an object included in the sensing information (Generating class/type information of an object included in the sensing information. see at least [¶050-051]);
a stationary object detector that detects a stationary object from among the objects on a basis of the class information, the stationary object moving with a probability less than or equal to a specified threshold (Detecting stationary objects from a set of sensed objects based on the object classification and the stationary object moving with a probability/likelihood less than a specified threshold. see at least [¶03, 071 & 074-075]);
Vora would be in a similar field as it also deals in the area of controlling a vehicle based on detected objects. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Imai to use the technique of having a class information generator that generates class information for an object included in the sensing information; a stationary object detector that detects a stationary object from among the objects on a basis of the class information, the stationary object moving with a probability less than or equal to a specified threshold as taught by Vora. Doing so would lead to improved control of a vehicle depending on the state of the vehicle (see at least [¶075-076]).
Imai and Vora do not explicitly teach and a position information calculator that calculates position information regarding a position of the vehicle on a basis of comparison of position information regarding a position of the stationary object to stored map information.
However, Chiba does teach a position information calculator that calculates position information regarding a position of the vehicle on a basis of comparison of position information regarding a position of the stationary object to stored map information (Calculating the self-position of the vehicle based in the comparison of the position of the stationary object/landmark to the stored map information. see at least [¶035-036 & 039-040]).
Chiba would be in a similar field as it also deals in the area of estimating the self-position of a vehicle using objects around the vehicle. Therefore, it would have been obvious to those having ordinary skill in the art before the effective filing date of the instant application to modify Imai and Vora to use the technique of having a position information calculator that calculates position information regarding a position of the vehicle on a basis of comparison of position information regarding a position of the stationary object to stored map information as taught by Chiba. Doing so would lead to a reduced processing load when estimating the self-position of the vehicle (see at least [see at least [¶05 & 055]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
CONTROL DEVICE FOR DRIVING ASSISTANCE DEVICE (US 20230242103 A1)
Dynamic Object Auto-labeling (US 12578469 B1)
Any inquiry concerning this communication or earlier communications from the examiner
should be directed to MOISES GASCA ALVA JR whose telephone number is (571)272-3752. The examiner
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor,
Faris Almatrahi can be reached on (313) 446-4821. The fax phone number for the organization were
this application or proceeding is assigned is 571-273-8300. Information regarding the status of published
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/MOISES GASCA ALVA/Examiner, Art Unit 3667
/FARIS S ALMATRAHI/Supervisory Patent Examiner, Art Unit 3667
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